Accuracy Estimation for Medical Image Registration Using Regression Forests

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Accuracy Estimation for Medical Image Registration Using Regression Forests

Conference Paper (2016)

Author(s)

Hessam Sokooti (Leiden University Medical Center)

Gorkem Saygili (Leiden University Medical Center)

Ben Glocker (Imperial College London)

Boudewijn Lelieveldt (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

Marius Staring (Leiden University Medical Center)

Image registration Uncertainty estimation Registration accuracy Regression forests

DOI related publication

https://doi.org/10.1007/978-3-319-46726-9_13 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:9fa960e0-0c68-47b5-8a6e-6f4cfbbc16de

More Info

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Publication Year

2016

Language

English

Pages (from-to)

107-115

Publisher

Springer

ISBN (print)

978-3-319-46725-2

ISBN (electronic)

978-3-319-46726-9

Event

Downloads counter

175

Abstract

This paper reports a new automatic algorithm to estimate the misregistration in a quantitative manner. A random regression forest is constructed, predicting the local registration error. The forest is built using local and modality independent features related to the registration precision, the transformation model and intensity-based similarity after registration. The forest is trained and tested using manually annotated corresponding points between pairs of chest CT scans. The results show that the mean absolute error of regression is 0.72 ± 0.96 mm and the accuracy of classification in three classes (correct, poor and wrong registration) is 93.4 %, comparing favorably to a competing method. In conclusion, a method was proposed that for the first time shows the feasibility of automatic registration assessment by means of regression, and promising results were obtained.